Surface Estimation Based on Lidar

In the past several years, the use of airborne laser systems or LIDAR for the rapid collection of digital terrain models (DTMs) has proliferated. Flood plain studies, contouring, road engineering projects, volumetric computations, ortho-photo production, and mapping for beach erosion are just some of the applications driving the demand for this technology. The ability of LIDAR systems to capture accurate spot heights at an extremely rapid rate is the principle reason behind LIDAR's success. Many applications, for example, contouring, require a bald-earth DTM. Unfortunately, the raw data points captured by LIDAR do not constitute a bald-earth DTM. Even though most LIDAR systems can measure "lastreturn" data points, these "last-return" points often measure ground clutter like shrubbery, cars, buildings, and even the canopy of dense foliage. Consequently, raw LIDAR points must be post-processed to remove these undesirable returns. The degree to which this post processing is successful is critical in determining whether LIDAR is cost effective for large-scale mapping applications. We present our approach to estimating bald-earth surfaces from LIDAR data. Our approach is different from typical approaches in that we estimate a surface based on the original LIDAR points while at the same time considering important supplementary information. This other information includes independently measured breaklines and surface categories. We use a least-squares adjustment with robust estimation similar to that proposed by (Kraus, Pfeifer, 1998). The surface model is represented using a triangular irregular network or TIN. We present examples from a real mapping project that demonstrate the success of this approach. Introduction LIDAR systems have become one of the prime methods for rapid collection of large-scale height data for various applications, especially in Europe where LIDAR is used for creating and updating national DTM’s. Although LIDAR technology is widely used by mapping companies, the reliable, efficient creation of accurate DTM’s from LIDAR measurements is problematic. (Huising, Gomes, 1998) identify two major problems: the elimination of systematic errors and the selection of ground points, i.e. the derivation of a bald-earth DTM from LIDAR measurements. The presence of systematic errors can often be observed between overlapping LIDAR strips. The modeling and elimination of these systematic errors is currently a topic of research (Burman, 2000). The second problem is the derivation of a bald-earth DTM from LIDAR measurements. LIDAR pulses measure not only on the ground but also ground clutter like shrubbery, cars, buildings, and tree canopies. Consequently, raw LIDAR points must be postprocessed to remove these undesirable returns. In this paper we paper we focus on the second problem, the derivation of a bald-earth DTM from LIDAR measurements. Previous Work Several publications deal with the problem of bald-earth DTM derivation from LIDAR measurements. Almost all of them either use one of the following two approaches or a combination of both. The first approach is a filtering Published in: Proceedings of the ASPRS Annual Conference. St. Louis, Missouri, April 2001.